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OPERATIONS AND SUPPLY CHAIN MANAGEMENT
Vol. 3, No. 2, May 2010, pp. 94-104
ISSN 1979-3561|EISSN 1979-3871
94
Distribution of Customer Perception Information
within the Supply Chain
Robert Schmitt
Laboratory for Machine Tools and Production Engineering WZL, RWTH Aachen University, Germany,
E-mail: [email protected]
Bastian Quattelbaum
Laboratory for Machine Tools and Production Engineering WZL, RWTH Aachen University, Germany,
E-mail: [email protected]
Björn Falk
Laboratory for Machine Tools and Production Engineering WZL, RWTH Aachen University, Germany,
E-mail: [email protected]
Abstract
The purpose of the paper is the elicitation of customers’ perception information regarding consumer goods
within different levels of accuracy. In the industry for consumer goods many efforts are carried out for the
elicitation of customer information, but a systematic preparation and distribution of the gathered information
is missing. Information with different level of accuracy circulates within a company but is not centralized for
corporate use. Based on a proposed holistic framework for objectifying customer information for supply chains’
product specifications study procedures have been developed and corresponded studies were conducted.
The results show that a gradual approach objectifies customers’ perception. Products can be divided into subelements by identifying perception clusters and quality attributes. First correlations between customers’
judgment and characteristics of quality attributes can be shown.
Keywords: : perceived quality, quality attributes, customer requirements, supply chain
1. Introduction
The market for consumer products is highly
competitive. Offered products are technically
excellent but are not able to differentiate themselves
via performance features from each other. When
comparing different products in the same industry,
it becomes evident that, on the one hand, there is a
narrow range of embodied technical features. On
the other hand, there is an assimilation of technical
product quality between products manufactured in
classical high-wage countries and those in upcoming
low-wage countries. These effects can be ascribed
to the uncompromising concentration on process
and technology improvements. That’s why even a
market leader has to detect new potentials for
successful products. (Lüthi, 2006)
The key factor for the development of successful
products is the customer. Companies have to gather
a holistic set of information regarding their products
from their customers. In order to be holistic, the set
of information needs to contain performance and
feature oriented requirements as well as subjective
criteria like sensory quality perception. Additionally,
it is essential to understand the influencing factors
Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
on customers’ quality judgment such as brand
image, price, or design. The future objective has to
be the offer of high-quality products that are also
perceived as high-quality by the customer. (Moss,
2006)
Companies and the whole Supply Chain have
to focus on the customer quality perception, the socalled perceived quality. Without the knowledge of
what most affects the customer and what he
perceives as high quality and harmonious, the
development and production of consumer goods is
like a lottery. This is especially when considered
against the background of the increasing number of
system suppliers in the extensive value added chain.
The paper at hand will give an answer regarding
the challenges of perceived quality. It shows that the
customer is the preferred measuring device to gather
comprehensive product requirements and describes
methods for information elicitation. A systematic
framework will be introduced to gather customer
information regarding their product perception and
disseminate it within the Supply Chain. Two case
study results show the application of the presented
framework.
2. Challenges to Perceived Quality
Due to the complex matter and interdisciplinarity of
the topic, a common understanding of the term
perceived quality is necessary. Numerous authors
from different fields of research have designated their
efforts to the field of customer perception. The
majority of them deal with the identification of
influences on the customer during his evaluation of
products. These influences arise from both extrinsic
and intrinsic quality cues of a product and its
environment, including social, aesthetic, and
functional aspects (Shapiro, 1970; Olson, 1972; Olson
and Jacoby, 1972; Zeithaml, 1988; Steenkamp, 1989;
Castleberry and McIntyre, 1992). The mostly
theoretical nature of the models makes application
in the product development process rather difficult.
To industrialize the topic more manageable, Schmitt
et al. (2008) define perceived quality as the result of
the cognitive and emotional comparison of a
customer’s conscious and unconscious experiences,
and thus expectations towards a product, and the
actually realized product attributes in a specific use
situation. This definition implicitly includes the
95
customer’s perception through his interdependent
(Lindstrom, 2005) human senses.
Measuring perceived quality and the resultant
perception of harmony demands an information
source that provides holistic data about product
requirements, both objective and subjective. It is
indispensable for the product development process
and the downstream production to define scales
which offer a spectrum to describe the customers’
product requirements in a detailed way. (Zalila et al.,
2005) Available information sources are the common
metrology for objective data, the assessment by
product experts for a subjective-objective judgment
and the customers’ evaluation which procures
subjective information. (Figure 1)
For a standardized and reproducible
measurement, the application of metrology would fit
best. But it is insufficient if the objective is to gather
comprehensive information including subjective
sensorial perception. Therefore, integration of human
beings is still necessary. The input of product experts
is important in later phases of the product development
process, such as the specification phase. The definition
of product specifications, including all objective and
subjective requirements, is the experts’ main task. The
customer therefore is the main source for information
elicitation regarding perception requirements. His
information is highly subjective and difficult to survey
but it is the key factor for successful products.
Customers don’t express their requirements
comprehensively and not in ways which allow them
to be used unfiltered and unprepared for the product
Figure. 1: Sources of Information
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Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
development process (Schmitt and Quattelbaum,
2009).
In research and industrial use there exist many
methods to gather information of the different
information sources, which try to link these with
corresponding technical parameters (Yannou, 2004).
In general, the recording of customers’ expectations
and requirements are on a high level of abstraction
as a consequence of the lack of detailed technological
product knowledge. This complicates the translation
of the customers’ information into specific product
parameters and results in a misinterpretation process
during product development. The elicitation and
preparation of subjective and objective information
is typically both cost- and time-intensive (van Hippel,
1994). In the following the different methods and their
deficits will be described briefly.
The means-end approach addresses the
personal-desire-complex and measures the functional
and psychosocial consequences activated by the
characteristics of products. Those consequences are
the foundation (= means) for the realization of the
overall individual desire imagination (= ends).
(Reinicke, 2004) The main part of means-end is the
“laddering”-approach, which gathers so called
means-end-chains. A qualified interviewer tries to
gather the probands’ desires of relevant product
characteristics with its implemented benefit for
different customer-groups. The means-end approach
lacks of a standardized procedure (especially the
interviews) and most of the probands aren’t aware of
their personal values. (Lorenzi, 2003)
Lead-user workshops develop long-term
customer requirements and trends. (Schröder et al.,
1997) Lead-users are innovation-inspirer, who today
have expectations regarding a product, which other
customers aren’t aware of. Based on technology
trends the lead-users will be chosen, by checking if
the probands already think in the direction of the
trends or if they have own innovations for this topic.
During different workshops the lead-users discuss
possible problems of the new products, cluster them
in fields of problems and develop problem-solving
concepts. All in all the deficit of the lead-user method
is the time-consummating, complicated and
confirmed identification of lead-user-probands.
(Gochermann, 2004)
In comparison to the lead-user workshops the
concept tests are used for the integration of the
Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
customer in the product development process while
interacting with a new product concept. Therefore
the customers are confronted with new product
designs visually or during interaction. Their opinions
will be gathered with qualitative and quantitative
survey methods. Concepts can be presented by
photos, components, prototypes or pre-series
products. Empirical results can be surveyed with
proband groups from 500 - 1000 purchasers.
(Schwarze, 2003) Concept test are cost-intensive and
with the number of probands the danger for loosing
product ideas to competitors increases. (Högl, 1996)
Supporting to the above mentioned methods
kansei engineering translates proactive customer
requirements, feelings and impressions regarding to
existing products or concepts in new product
concepts and parameters. (Schütte, 2004; Blecker,
2003) Therefore two multi-dimensional vector spaces
are applied and connected with each other. The first
space, the semantic space, offers for one superior
product idea “kansei-words”, which describe the idea
with semantic word pairs. The second space, space
of properties, includes all product properties, which
have influence on the satisfaction of the customer.
The connection of both spaces occurs by the
description of each property with different kanseiwords. (Schütte, 2004) The specific problem with
kansei-engineering is the reduction of the customer
expression to the spoken words and the possible loss
of necessary implicit requirements. (Schütte, 2004)
The conjoint-analysis aims on the evaluation of
the customer benefit of a product by all important
product characteristics. Therefore it is supposed that
the overall benefit is the sum of the discrete benefit
of the characteristics. (Eversheim, 2003; Dietz, 2000)
From the frequency of characteristic mentions and
the regarding product assessment the benefit of each
component and characteristic can be inferred.
(Eversheim, 2003) With increasing product
complexity the number of characteristics influencing
the overall judgement increases too and additionally
the pre-definition of product characteristics by the
company harbours danger because of a formative
influencing. (Dietz, 2000)
The kano-method is most popular method for
gathering customer requirements towards new
products or improvements. It clusters the
requirements regarding product characteristics into
must-be, performance and attractive features. The
Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
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Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
fulfilment of these three clusters offers completely
different levels of customer satisfaction. Kano, 1984;
Löfgren, 2005) For the identification of the kanocluster the kano-survey is used. Probands are asked
if they are satisfied when a product characteristic is
fulfilled sufficient and when it is unsufficient. These
two questions are asked during the kano-suvey for
each product characteristic. With this pair of question
an assignment to the kano-clusters can be applied.
(Zultner and Mazur, 2006) The kano-model offers the
possibility to cluster product characteristics, but is
mostly feature driven and does not include the
customers’ sensorial perception. Additionally the
subjective and unconscious character of customer
requirements has to be handled (Tsiotsou, 2006).
One major problem of all methods is the
establishment of “information islands” inside the
organization. Instead of structured preparation and
distribution of customer information, a great number
of unlinked initiatives work with customers and the
topic perceived quality. These different initiatives are
spread over the whole product life cycle. The elicited
information is in most cases used locally and thereby
great potentials for a proper adjustment of the supply
chain are wasted (Betzold et al., 2008).
The Customer Requirements Bullwhip Effect
Customer requirements and quality perceptions
influence the internal production structure in a supply
chain. Production is divided cross-company but its
elements are aligned with each other. The management
of this production is called flow oriented planning and
is illustrated within value-adding chain diagrams. As
a result of the divided production, interfaces occur
where problems like miscommunication, duplication
or nonspecific responsibilities exist. The supply chain
management is responsible for the elimination of
these problems as well as general improvements.
(Ayers, 2000) Due to the high quantity of quality
perception information and the new field of
information, it is a new challenge for the information
distribution within the supply chain. The reduction
of costs and development time is a main objective in
supply chains of consumer products (Erdmann, 2002;
Arndt, 2006; Metz, 1998; Lambert and Cooper, 2000).
Proper information about quality perception helps
to reach this objective by focusing on parameters
relevant to the customer and avoiding overengineering.
The so-called bullwhip effect is a vital problem
of supply chain management and describes the
difficulties of handling customer information within
a value-added chain (Lee and Padmanabhan, 2004).
Nowadays, the information distribution regarding
perceived quality generally stops at the original
equipment manufacturer (OEM) or the system
supplier. The lower levels of the value-added chain
hardly have knowledge about customer
requirements. According to Vojak and Suárez-Núñez
(2004) the information flow of perceived quality
attributes forces a customer requirements bullwhip
effect. The OEM gathers customer requirements
about a product and its attributes. He interprets the
requirements and specifies the quality attributes. At
this moment the connection to the customer is lost
for lower levels of the supply chain. They don’t know
the original statement of the customer and
additionally there is no knowledge given which
quality attributes interact with each other from the
customers’ point of view. Thus, the suppliers of lower
supply chain levels have no idea with which other
product components or quality attributes their
products should fit together. This has the effect of a
high amplitude of the bullwhip (Figure. 2).
Figure. 2: Customer Requirements Bullwhip Effect
Customer
Customer
OEM
OEM
System
System
supplier
supplier
Component
Component
supplier
supplier
Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
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Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
The customer requirements bullwhip effect
shows the importance of holistic information
distribution regarding perceived quality within the
supply chain. For the reporting of technical and
additionally perceived quality information even more
information has to be handled. This is accompanied
by a poor predictability. The factor perceived quality,
the unconscious subjective requirements regarding
the sensory perception, increases this poor
predictability (Schmitt and Quattelbaum, 2009).
3. Systematic Research Framework
The integration of customers and their data in the
supply chain has to be established. The research
questions concerning the integration of customer
perception data are:
1. How can customers be integrated in product
assessments regarding perception?
2. Can customers deliver enough information
without prior qualification?
3. Is the level of accuracy of this data sufficient
for handling in a supply chain?
4. To which amount and information detail
level has the customer to be integrated?
The concretization of the different research
questions shows links between problems of practice
and focuses on different research fields. Thus the
central part of the scientific methodology is a heuristic
framework (Kubicek, 1977). Components of this
framework are the objects information elicitation,
perception, customers, products and supply chain.
For safeguarding the production of attractive
quality of products a five-stage framework according
to Schmitt et al. (2009) for integrating perceived
quality information into the product development is
proposed. Figure 3 shows the proposed framework
for objectifying customer information into the product
specification. The framework is divided into five
stages: Overall impression, perception cluster, quality
attribute, descriptor and technical parameter. To achieve
an improvement of attractive quality, detailed
knowledge about the multi-sensory-perception of the
product needs to be developed.
The first interaction of a customer with a product
and the regarding impressions are reflected in the
overall impression. Only to a small extent product
details and individual components are the reasons
for this impression. The overall impression is the
foundation for analysis of a detailed multi-sensorial
product perception. Taking factors like brand image,
price or product harmony into account, no
conclusions about the real causes of a customer
judgment can be made by the overall impression
(Macdonald, 2001).
Consecutively to the overall impression, as the first
stage of accuracy, perception clusters encompass
different product components that are perceived
together as one unit, on the one hand through human
sensory perception with one sense (e.g. tune of
acoustic) and on the other hand on the level of
superposition of senses (e.g. congruence in acoustics
and haptics). For highly complex products like
airplanes or cars the definition of perception clusters is
essential to divide products into manageable areas
Figure. 3: Systematic Framework
Perception
cluster
Quality
attribute
Technical
parameter
Descriptor
fo rc e
Overall
impression
P4
P5
P3
P1
P7
P2
P6
P8
P0
travel
Example
Car
Interior
Switch
Force progression
Spring
Perception
Harmonic
Single Unit
High
quality
-
-
Type of
information
Subjective
Subjective
Subjective
Partly-objective
Objective
Naive customer
Naive customer
Customer/Expert
Expert/Metrology
Construction
Source
Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
of interest. Perception mismatches within a cluster
have negative influence on the customer cluster
judgment and the overall impression.
The perceived quality of a perception cluster is
determined by quality attributes. They have a strong
influence on the overall quality judgment of a
product and are represented by multi-sensorial
product characteristics. The most important
requirement to these attributes is the characteristic
of scalability. If no scale for an attribute can be found,
it still can be divided into sub-attributes (e.g. surface
=> surface slippery). Overall the stage from perception
clusters to quality attributes is the first detailing of a
product regarding perception, which helps
companies to avoid over-engineering and to focus
on customer relevant product attributes.
The next stage of the framework is connected
with a change in the data sources. Quality attributes
are the last level of accuracy which can be an
executed by naïve customers. Descriptors define a
quality attribute in terms of “qualified” customers.
For the definition of descriptors, which means
objectifying attractive quality, “descriptive methods”
derived from sensory analysis (Piper and Scharf,
2006) are necessary. During the application of these
“descriptive methods” the customers describe their
perception as specifically as possible while
comparing quality attributes presented with different
characteristic values. In this manner, quality attributes
get described in the customers’ vocabulary in a
standardized way, correspondent with technical
specifications to the greatest possible degree.
The last stage of the framework realizes the
connection between the defined descriptors and
technical parameters that are relevant for construction,
production and installation. Consequently, the
descriptors have to be measured by adequate and
capable metrology. Not for each descriptor common
metrology is available. New measurement methods
and strategies have to be developed and validated.
Companies, especially the construction and
development departments, have to identify the set
of technical parameters which impact on a specific
descriptor. The activated effect for the customer can
be stated by variation of the identified technical
parameters combined with metrology. Beside the
relationship between the customer judgments and
the parameter variations for knowing a direction of
product optimization, appropriate studies can
99
elucidate the tolerance between a positive and a
negative judgment for a certain quality attribute or
descriptor. For industrial use, this point is far more
interesting, because of knowing the range where to
locate their product attributes.
The elicited information out of the presented
framework has to be distributed throughout the
whole supply chain and the prevalent isolated
application of information without any systematic
frame has to be replaced (Aslandis and Korell, 2003).
The high number of interfaces within a company
and throughout the supply chain have to be
prepared to transport perceived quality information.
Without defining the interfaces, an increased
imprecision and data loss in processing would
follow. Present approaches focus on snapshots for
short term benefits and local improvements instead
of a sustainable progress (Reinicke, 2004). In fact,
the product specifications which are elaborated by
the OEM are vague or become even more so while
being passed on through different tiers of suppliers.
This is caused by the pre-described
misinterpretations of customer requirements and the
missing tolerances when dealing with perceived
quality data and leads to the mentioned “customer
requirements bullwhip effect” (Schmitt and
Quattelbaum, 2009). The stages of the framework
in chapter 3.1 deliver information with different
levels of accuracy about customers’ perception. The
long-term objective is the distribution of this
information within the supply chain.
4. Methodology
The paper at hand focuses on the application of the
framework stages perception clusters and quality
attributes. Survey methods for gathering of conscious
and unconscious information are:
- Free interviews,
- Observation during interaction,
- The “think-aloud”-method,
- Workshops in small-sized groups
- Empirical studies.
Free single interviews were chosen as method
for the framework stage perception clusters. The
decision against a standardized and automatically
analyzable questionnaire was based upon the
influencing character of this answering type.
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Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
Customers should have the chance to express their
own opinion and not with pre-formulated phrases.
A combined observation by the interviewer as well
as with different cameras allowed the retrospective
examination of points of interest in the interview.
The “think-aloud”-method was used for both
framework stages and can be applied in different
settings. The range of settings can vary from
presenting products by pictures, videos, sounds to
the interaction with the real product. Probands
express their associations and thoughts absolutely
free and unfiltered, during being in contact with a
product or an impulse. Besides the gathering of
information about enthusing or frustrating product
attributes, general knowledge about product
handling and usability can be surveyed with the
unfiltered expressions. The method represents the
expressed thought-process sequentially and thereby
facilitates the disclosure of causality within the
customer’s assessment procedure by the product
developers. (Buber 2007)
For a successful application of the framework
stage quality attributes a combination of different
methods is suggested. A two step procedure for the
survey of a comprehensive set of quality attributes
is efficient. In the beginning the invited probands
appoint quality attributes with the “think-aloud”method and discuss the defined personal attributes
with all probands together in a moderated
workshop. This workshop concept helps to discuss
the different findings and negotiate a common active
vocabulary. In a consecutive study with a larger
number of probands the quality attributes can be
evaluated empirically.
5. Study Procedures, Participants,
and Results
Cluster Identification Study
The identification study for perception clusters was
conducted by the example of a compact car interior.
As the car interior is a complex part of the car the
observation object was reduced to the front cabin
(starting at the B-pillar), including driver and front
passenger. The assessment focus was on haptical and
optical perception.
The applied qualitative interview was a semistructured interview, divided into four main parts:
Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
- Free interaction
- Binning components
- Interference analysis
- Target perception clusters
Opening, the probands were asked for a period
of free interaction with the car interior. They had to
articulate, applying the “think-aloud” method, their
first expressions and opinions. In the second part of
the interview, the probands defined which interior
components belong together in their opinion
concerning haptics and optics. They got accustomed
to possible clusters and got an idea what binned
components they like. With questions to the reasons
of binning components, it was possible to get
information what establishes a cluster from
customers’ point of view. Subsequently, the
interview focused on interferences perceived by the
probands. Defined interference were for instance
caused by different colours, different topography
between parts or clearances. Jointed with the
customer judgment the stated interferences allowed
a conclusion concerning which components should
belong together to a cluster. Throughout the last
interview part, the probands were asked to name
their target perception cluster for a harmonious
interior. In this part the interviewer reflected the
before expressed clusters and interferences to secure
the completeness of the cluster set.
The study was conducted in a defined,
reproducible setting and lasted over two weeks, with
approx. 14 time-slots per day. Beside written
interviewer documentation, the study was
documented by video and audio. In average 20
minutes lasting interviews 139 probands, aged
between 18 and 60 years, were interviewed and
recorded out of three camera perspectives. The
sample size allowed differentiated analysis in nine
evaluation groups. (Table 1) In analysis the customer
expressed phrases were semantically clustered and
the frequency of each cluster was evaluated.
Thereby seven main perception clusters
(threshold 15 %) for the compact car were generated.
Beside, for each cluster interface components were
evaluated, which should fit harmonically to the
cluster regarding optical and haptical perception.
The seven analyzed main perception clusters were:
(1) dashboard, (2) steering wheel incl. stalk switches,
(3) gear box connected to hand brake, (4) instrument
Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
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Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
Table 1: Evaluation Groups
Name of Group
Participants
Gender Male
118
Gender Female
21
Age “Young” (< 29 years)
107
Age “Experienced” (> 28 years)
32
Car Type “Compact Car”
48
Car Type “Other”
91
Frequency of driving “seldom”
91
Frequency of driving “often”
48
Total participants
139
cluster panel, (5) doors, (6) footwell and (7)
headliner.
The comparison of the nine evaluation groups
showed a homogenous estimation of the analyzed
seven perception clusters (Figure 4).
Quality Attribute Study
On the basis of the main perception clusters quality
attributes have to be developed as the next stage of
the proposed framework. Therefore a new study
design according to the common approach of quality
descriptive analysis out of the food industry sector
was adapted and applied. This study design was
used for the perception cluster “steering wheel”. As
observation object six different steering wheels from
different classes of cars were presented. As
mentioned in Chapter 4 the two steps of workshop
and empirical study were employed. For the
workshop on the one hand a wide demographic
range of people had to be acquired and on the other
hand only a few probands should participate out of
time- and cost-consummating reasons. Twelve
people in three different groups of age (18-30 years,
31-50 years and 51-70 years) were acquired. Each
group included four probands, equally men and
women. In the beginning the invited probands
appoint quality attributes with the “think-aloud”method. The probands documented their personal
quality attributes during interaction with the steering
wheels in written form. Additionally to the written
quality attributes they suggested a suitable scale for
each attribute to secure the scalability. After this first
step, the defined personal attributes were discussed
by all probands in a moderated workshop. During
this workshop the probands clustered their set of
attributes, entitled these clusters with an attribute
name and discussed and named adequate scales. As
a result an agreed set of quality attributes and linked
scales was defined, described in the language of the
customers. The moderating experts learned, by
participating and not influencing, the customers`
active vocabulary. After this workshop a set of 44
quality attributes was available. With these 44
attributes the twelve probands assessed the six
steering wheels. For each steering wheel all 44
attributes were characterized with the defined scale
and the design was judged. In statistical analysis the
44 attributes were reduced to applicable an amount
of attributes. Three reduction principles were used:
Figure. 4: Perception Clusters within Evaluation Groups
total
male
female
young
experienced
frequent driver
infrequent driver
compact car
other classes
max
I/P
ICP & AC
steering wheel
& stalk switch
gear shifter
& handbrake
doors
headliner
foot well
Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
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Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
- Relation classes
- Standard deviation
- Rating Pareto
In a first analysis it was examined if all twelve
probands related the steering wheel against each
other in the same way. That means not to characterize
the steering wheel equally, but to put them in the same
relation. Secondly the normalized standard deviation
of all probands for the different attributes was
calculated and compared to a threshold of 0.7. The
normalized standard deviation stated compared to
the relation classes how equally the probands
characterized the steering wheels. At last the ranking
of the probands where analysed with Pareto-method.
The probands had the chance to assign 16 points to
the 44 attributes to show their importance ranking.
With the accumulated results the Pareto-analysis was
applied. Following these three principles a reduction
of the attributes to number of 26 attributes was
possible, which were the attribute set for the
consecutive empirical study.
This study was performed with 64 people, aged
between 18 and 40 years. The objectives were the
review of the identified quality attributes developed
in the workshop and the assessment of the attributes
for each steering wheel. Therefore the probands
named in a short period of time quality attributes of
the product. The set of attributes of all probands got
compared with the workshop attributes. The degree
of overlap determined if the defined quality attributes
are sufficient to reflect the customers’ mindset. The
Figure. 5: Reduction of Quality Attributes
44
26
approx. 600 attributes announced in
proband study by 64 probands:
! Fit to workshop attributes 88%
! Fit to reduced attributes 68%
Set of attributes in sufficient
Table 2: Important Quality Attributes and their Impact
Attributes
from 1
to 6
CorrelationIndex
Country
Clearance/Gaps
small
wide
0,795
small = good
Distance between
hand position and
switches
near
wide
0,780
near = good
-0,754
perceptible =
of good
Haptical perceptibility
of switches
not perceptible perceptible
Material interfaces
smooth
rough
0,711
smooth = good
Slippery of steering
wheel rim
no slippery
slippery
0,615
no slippery =
good
Size of visibility space
small
big
-0,539
big = good
Protrusion of airbag
little
big
0,526
little = good
Required force for horn
little
much
0,510
little = good
Visibility of stitching
not visible
visible
0,080
no significance
Surface roughness of
steering wheel rim
plain
rough
-0,012
no significance
evaluated overlap was 68 % compared to the 26
reduced attributes and 88 % to the 44 general
attributes (Figure 5). The overlap analysis was
performed by comparing the announced attributes
semantically.
Additionally the empirical study offered the
chance to get a customer judgment for each quality
attribute and the overall product. The probands were
asked to fill a questionnaire-frontend where all scales
for each attribute, visualized by a pictogram, were
listed and to give the judgment with a 6 point Likert
scale.
With these judgments correlation analysis
between a quality attribute characteristic value and
the direction of judgment were made, which is the
foundation for specific product improvements. Ten
of the 26 quality attributes showed significant (twosided significant level of 0.01) or no significant
correlation between judgment and correlation with
thresholds of 0.5 and 0.1. For example the quality
attribute “clearances/gaps” had the highest
correlation index with 0.795, which means small gaps
lead to a positive judgment. In contrast for the
attribute “visibility of stitching” no significance could
be found. (Table 2) For the left 16 quality attributes
further statistical analysis, like Structural-EquationModelling, is necessary to show interdependencies
between the different attributes.
Schmitt et al: Distribution of Customer Perception Information within the Supply Chain
Operations & Supply Chain Management 3 (2) pp 94-104 © 2010
6. Conclusion and Forecast
The presented paper shows an industrial framework
to identify product relevant information of quality
perception of the customer and gives instructions for
the information distribution within the supply chain.
However, the framework still holds room for
adaption. Applying the framework through all five
stages and in different branches of consumer products
will advance its impact and prove its universality.
Further studies for the application of the stage of
quality attributes (e.g. with steering wheels and
cordless phones) show that the descriptive methods
adapted from the sensory analysis deliver very
detailed and nearly objective information from the
source “customer”.
The usage of descriptive methods and the strong
integration of the customer (qualifying them to
experts), with a minimal focus on his product
judgment, reduces the influence of social, functional
and aesthetical aspects, such as brand image and
design. Nevertheless, a consideration of these factors
has to take place within the discussion on perceived
product value (Schmitt and Steinmeier, 2008).
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Prof. Dr.-Ing. Robert Schmitt, born in 1961, is head of the Chair of Metrology and Quality Management
and member of the board of directors for the Laboratory for Machine Tools and Production Engineering
WZL of the RWTH Aachen University. He is also head of the department Production Metrology and Quality
of the Fraunhofer Institute for Production Technology IPT as well as member of the board of directors of the
Fraunhofer IPT.
Dipl.-Ing. Bastian Quattelbaum, born in 1979, is research assistant at the Chair of Metrology and Quality
Management for the Laboratory for Machine Tools and Production Engineering WZL of the RWTH Aachen
University. He is member of the research group “Perceived Quality and Product Value Management”.
Dipl.-Ing. Dipl.-Wirt. Ing. Björn Falk, born in 1980, is research assistant at the Chair of Metrology and
Quality Management for the Laboratory for Machine Tools and Production Engineering WZL of the RWTH
Aachen University. He is member of the research group “Perceived Quality and Product Value Management”.

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